How I work
AI projects fail in production, not in the demo, and most fail in committee before that. My process is built to get past both: fast to value, with the people who matter bought in, and a real system at the end.
Dig in fast (days, not weeks)
I get into your actual systems, data, and goals fast. We find the use cases that are genuinely high-ROI and genuinely buildable, and kill the ones that aren't before they waste anyone's time or budget.
Get buy-in
The best build dies in committee if the right people aren't aligned. I help you make the case to stakeholders in their language, with honest tradeoffs, so the work has air cover instead of stalling.
Build it
Then I actually build. Code, integrations, evals, guardrails. Working software running in your stack, not recommendations in a doc. I move fast and I don't waste your time or mine.
Hand off
Your team owns it. You get the evals, docs, and the actual understanding to run and extend it. No black box, no lock-in, no permanent dependency on me.
How I'll feel different
- Outcomes over output, measured in hours saved and things shipped, not tokens or slides.
- Evals first, nothing ships without a way to know it's actually working.
- Honest about tradeoffs, if AI isn't the right answer, I'll say so.
- Your team owns it, I transfer knowledge, not lock-in.
- Safety where it counts, extra rigor the moment an agent touches money or prod.
Ways to work together
- Diagnostic sprint, a fast, paid dig-in that ends with a prioritized, costed plan of what's worth building (and what isn't).
- Build engagement, I build a specific system or workflow end-to-end and hand it to your team.
- Embedded / fractional, your part-time AI engineer for a stretch, shipping alongside your team.